Popularity of the Internet and refinement of issues like Internet security has encouraged selling and purchasing of items over the Internet. Several online stores exist which facilitate buying and selling of items such as books, clothes, shoes, music cassettes, videos and other consumer goods. When customers visit an online store, they are presented with a list of items available in the store, as per the requirement of the user. The list of items grouped together in a systematic manner for presentation to the customer is also known as a catalog.
Design of catalog is an important issue because the placement of products influences the sales of the products. Customer's response in terms of purchase of a product changes if the catalog changes. For example, if two similar products are placed very near to each other in a catalog then the sales of one can increase or decrease the sales of the other. Similarly, a product placed on the top of the catalog is expected to get a better response as compared to a similar product at the bottom. Also, presenting items to a customer that the customer does not have interest in, can prove to be an improper sales strategy. Therefore, designing a catalog is an important issue when it comes to following a proper sales strategy.
Catalog design is inherently related to product recommendation where product attributes and customer attributes are considered. Several techniques exist that facilitate recommendation of products to users. Some of these will be discussed hereinafter.
United States patent application number 20020019763A1 titled “Use of Product Viewing Histories of Users to Identify Related Products” deals with item presentation on websites. This patent discloses various methods for monitoring user-browsing activities that indicate user interests in particular products. This information is used to identify relationship among products and to develop a table for mapping products to sets of related products. This table is then used for product recommendations.
U.S. Pat. No. 6,266,649, titled “Collaborative Recommendations using Item-Item Similarity Mappings” is assigned to Amazon.com, Inc. This patent discloses a recommendation service that recommends products to users based on a set of products known to be of interest to the user. The recommendations are based on collective interests of a group of users.
United States patent application number 20020161664A1 titled “Intelligent Performance-Based Product Recommendation System”, also deals with item presentation on websites. This patent discloses a system and method for recommending products from a predefined population of commercially available products based on user demand and product responsiveness patterns.
A publication titled “A personalized recommender system based on web usage mining and decision tree induction”, by Yoon Ho Cho, Jae Kyeong Kim and Soung Hie Kim (http://monkey.icu.ac.kr/sslab/course/ICE720/data/subjectPresentation/APersonalizedRecommen derSystem.pdf), describes a method for personalized recommendation to users with a list of products obtained by applying data mining techniques to discover user behavior patterns based on web data.
Internet website, http://www.Catalog.com, offers manual placement of products in catalogs offered on the web. Catalog.com has a Directory Community that allows customers to go through each of their communities and sub-communities to find the most specific and proper placement for their products.
Lastly, a news article titled “The catalog store—Catalog shop must operate by the book—and calendar”, by Lisa Biank Fasig (http://www.enquirer.com/today/business.html), describes catalogs by Frontgate that are generated after examining yearlong purchase trends and patterns. These trends are used to place products in a catalog so as to influence the number of products being ordered. These catalogs use past user transactions to decide current placement of items in the catalog.
Although the products, patents and publications discussed above propose techniques for design of catalogs, they suffer from one or more of the following drawbacks. None of the above-mentioned art provides an end-to-end, dynamic and automatic method for adapting catalogs to changing user demands. Also, these techniques do not provide for optimizing the relative placement of products in a catalog for maximizing the revenue or sales or number of clicks.
Therefore, in light of the existing art, there is need for a method and system for designing a catalog based on responses of users. Also, there is need for a method and system for automatically designing a catalog with optimized placement of products in order to maximize the revenue or sales or number of clicks.